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Teaching machines to read and comprehend

Abstract:

Teaching machines to read natural language documents remains an elusive challenge. Machine reading systems can be tested on their ability to answer questions posed on the contents of documents that they have seen, but until now large scale training and test datasets have been missing for this type of evaluation. In this work we define a new methodology that resolves this bottleneck and provides large scale supervised reading comprehension data. This allows us to develop a class of attention b...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Oxford college:
Linacre College
Role:
Author
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Publisher:
Neural Information Processing Systems Publisher's website
Journal:
Advances in Neural Information Processing Systems Journal website
Volume:
28
Pages:
1693-1701
Publication date:
2015-12-01
Acceptance date:
2015-08-09
ISSN:
1049-5258
Source identifiers:
527451
Keywords:
Pubs id:
pubs:527451
UUID:
uuid:050e7840-1ff3-49db-8d36-e83ed0adf8f7
Local pid:
pubs:527451
Deposit date:
2016-10-16

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